Claudia Klüppelberg
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View article: Causal analysis of extreme risk in a network of industry portfolios
Causal analysis of extreme risk in a network of industry portfolios Open
We provide a comprehensive review of causal dependence through a max-linear structural equation model. Such models express each node variable as a max-linear function of its parental node variables in a directed acyclic graph and some exog…
View article: Copula methods for modeling pair densities in density functional theory
Copula methods for modeling pair densities in density functional theory Open
We propose a new approach towards approximating the density-to-pair-density map based on copula theory from statistics. We extend the copula theory to multi-dimensional marginals, and deduce that one can describe any (exact or approximate)…
View article: Estimating a directed tree for extremes
Estimating a directed tree for extremes Open
We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of …
View article: Heavy-tailed max-linear structural equation models in networks with hidden nodes
Heavy-tailed max-linear structural equation models in networks with hidden nodes Open
Recursive max-linear vectors provide models for causal dependence between large values of random variables that are supported on directed acyclic graphs, but the standard assumption that all nodes of such a graph are observed can be unreal…
View article: Conditional independence in max-linear Bayesian networks
Conditional independence in max-linear Bayesian networks Open
Motivated by extreme value theory, max-linear Bayesian networks have been recently introduced and studied as an alternative to linear structural equation models. However, for max-linear systems the classical independence results for Bayesi…
View article: Causal inference for extremes on river networks
Causal inference for extremes on river networks Open
<p>Causal inference for extreme aims to discover cause and effect relation between large observed values of random variables. This is a fundamental problem to in many applications, from finance, engineering risks, nutrition to hydrol…
View article: Causal Discovery of a River Network from its Extremes.
Causal Discovery of a River Network from its Extremes. Open
Causal inference for extremes aims to discover cause and effect relations between large observed values of random variables. Over the last years, a number of methods have been proposed for solving the Hidden River Problem, with the Danube …
View article: Estimating a Latent Tree for Extremes
Estimating a Latent Tree for Extremes Open
The Latent River Problem has emerged as a flagship problem for causal discovery in extreme value statistics. This paper gives QTree, a simple and efficient algorithm to solve the Latent River Problem that outperforms existing methods. QTre…
View article: Estimating a Directed Tree for Extremes
Estimating a Directed Tree for Extremes Open
We propose a new method to estimate a root-directed spanning tree from extreme data. A prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects o…
View article: Indirect inference for time series using the empirical characteristic function and control variates
Indirect inference for time series using the empirical characteristic function and control variates Open
We estimate the parameter of a stationary time series process by minimizing the integrated weighted mean squared error between the empirical and simulated characteristic function, when the true characteristic functions cannot be explicitly…
View article: Recursive max-linear models with propagating noise
Recursive max-linear models with propagating noise Open
Recursive max-linear vectors model causal dependence between node variables by a structural equation model, expressing each node variable as a max-linear function of its parental nodes in a directed acyclic graph (DAG) and some exogenous i…
View article: Explicit results on conditional distributions of generalized exponential mixtures
Explicit results on conditional distributions of generalized exponential mixtures Open
For independent exponentially distributed random variables $X_i$ , $i\in {\mathcal{N}}$ , with distinct rates ${\lambda}_i$ we consider sums $\sum_{i\in\mathcal{A}} X_i$ for $\mathcal{A}\subseteq {\mathcal{N}}$ which follow generalized exp…
View article: Ruin probabilities for risk processes in a bipartite network
Ruin probabilities for risk processes in a bipartite network Open
This article studies risk balancing features in an insurance market by evaluating ruin probabilities for single and multiple components of a multivariate compound Poisson risk process. The dependence of the components of the process is ind…
View article: Identifiability and estimation of recursive max‐linear models
Identifiability and estimation of recursive max‐linear models Open
We address the identifiability and estimation of recursive max‐linear structural equation models represented by an edge‐weighted directed acyclic graph (DAG). Such models are generally unidentifiable and we identify the whole class of DAG …
View article: Estimation of causal continuous‐time autoregressive moving average random fields
Estimation of causal continuous‐time autoregressive moving average random fields Open
We estimate model parameters of Lévy‐driven causal continuous‐time autoregressive moving average random fields by fitting the empirical variogram to the theoretical counterpart using a weighted least squares (WLS) approach. Subsequent to d…
View article: Semiparametric estimation for isotropic max-stable space-time processes
Semiparametric estimation for isotropic max-stable space-time processes Open
Regularly varying space-time processes have proved useful to study extremal dependence in space-time data. We propose a semiparametric estimation procedure based on a closed form expression of the extremogram to estimate parametric models …
View article: Time series of functional data with application to yield curves
Time series of functional data with application to yield curves Open
We develop time series analysis of functional data observed discretely, treating the whole curve as a random realization from a distribution on functions that evolve over time. The method consists of principal components analysis of functi…
View article: Estimation of causal CARMA random fields
Estimation of causal CARMA random fields Open
We estimate model parameters of Lévy-driven causal CARMA random fields by fitting the empirical variogram to the theoretical counterpart using a weighted least squares (WLS) approach. Subsequent to deriving asymptotic results for the vario…
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Issue Information Open
No abstract is available for this article.
View article: Indirect Inference for Lévy‐driven continuous‐time GARCH models
Indirect Inference for Lévy‐driven continuous‐time GARCH models Open
We advocate the use of an Indirect Inference method to estimate the parameter of a COGARCH(1,1) process for equally spaced observations. This requires that the true model can be simulated and a reasonable estimation method for an approxima…
View article: Issue Information
Issue Information Open
No abstract is available for this article.